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   "source": [
    "# 活动热度数据（event_attendees.csv）处理\n",
    "（只取训练集和测试集中出现的用户ID）\n",
    "\n",
    "数据来源于Kaggle竞赛：Event Recommendation Engine Challenge，根据\n",
    "events they’ve responded to in the past\n",
    "user demographic information\n",
    "what events they’ve seen and clicked on in our app\n",
    "用户对某个活动是否感兴趣\n",
    "\n",
    "竞赛官网：\n",
    "https://www.kaggle.com/c/event-recommendation-engine-challenge/data\n",
    "\n",
    "\n",
    "event_attendees.csv文件：共5维特征\n",
    "event_id：活动ID\n",
    "yes, maybe, invited, and no：以空格隔开的用户列表，\n",
    "分别表示该活动参加的用户、可能参加的用户，被邀请的用户和不参加的用户."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "import scipy.sparse as ss\n",
    "import scipy.io as sio\n",
    "\n",
    "#保存数据\n",
    "import cPickle\n",
    "\n",
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "总的用户数目超过训练集和测试集中的用户，\n",
    "为节省处理时间和内存，先去处理train和test，得到竞赛需要用到的事件和用户\n",
    "然后对在训练集和测试集中出现过的事件和用户建立新的ID索引\n",
    "先运行user_event.ipynb,\n",
    "得到事件列表文件：PE_userIndex.pkl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取之前算好的测试集和训练集中出现过的活动"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of events in train & test :13418\n"
     ]
    }
   ],
   "source": [
    "#读取训练集和测试集中出现过的事件列表\n",
    "eventIndex = cPickle.load(open(\"PE_eventIndex.pkl\", 'rb'))\n",
    "n_events = len(eventIndex)\n",
    "\n",
    "print(\"number of events in train & test :%d\" % n_events)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# user_friends.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#读取数据\n",
    "\"\"\"\n",
    "  统计某个活动，参加和不参加的人数，计算活动热度\n",
    "\"\"\"\n",
    "\n",
    "#活动活跃度\n",
    "eventPopularity = ss.dok_matrix((n_events, 1))\n",
    "    \n",
    "f = open(\"event_attendees.csv\", 'rb')\n",
    "\n",
    "#字段：event_id,yes, maybe, invited, and no\n",
    "f.readline() # skip header\n",
    "\n",
    "for line in f:\n",
    "    cols = line.strip().split(\",\")\n",
    "    eventId = str(cols[0])   #event_id\n",
    "    if eventIndex.has_key(eventId):\n",
    "        i = eventIndex[eventId]  #事件索引\n",
    "        \n",
    "        #yes - no\n",
    "        eventPopularity[i, 0] = \\\n",
    "          len(cols[1].split(\" \")) - len(cols[4].split(\" \"))\n",
    "    \n",
    "f.close()\n",
    "    \n",
    "eventPopularity = normalize(eventPopularity, norm=\"l1\",\n",
    "      axis=0, copy=False)\n",
    "sio.mmwrite(\"EA_eventPopularity\", eventPopularity)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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    {
     "data": {
      "text/plain": [
       "matrix([[  0.00000000e+00],\n",
       "        [ -8.51191092e-05],\n",
       "        [  3.45077470e-05],\n",
       "        ..., \n",
       "        [  8.05180763e-05],\n",
       "        [  1.77139768e-04],\n",
       "        [  0.00000000e+00]])"
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "eventPopularity.todense()"
   ]
  },
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   "execution_count": null,
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